WO2011005783A2 - Image-based surface tracking - Google Patents
Image-based surface tracking Download PDFInfo
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- WO2011005783A2 WO2011005783A2 PCT/US2010/041096 US2010041096W WO2011005783A2 WO 2011005783 A2 WO2011005783 A2 WO 2011005783A2 US 2010041096 W US2010041096 W US 2010041096W WO 2011005783 A2 WO2011005783 A2 WO 2011005783A2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
- G06T7/55—Depth or shape recovery from multiple images
- G06T7/593—Depth or shape recovery from multiple images from stereo images
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/20—Image signal generators
- H04N13/204—Image signal generators using stereoscopic image cameras
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
- G06T2207/10021—Stereoscopic video; Stereoscopic image sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N2013/0074—Stereoscopic image analysis
- H04N2013/0081—Depth or disparity estimation from stereoscopic image signals
Definitions
- the technology relates to the field of image-based navigation.
- GPS devices face the challenging task of maintaining accurate localization, due to the lack of reception from the GPS satellites.
- We present an application which we call "ground tracking", that can recover the 3D location of an image capturing device.
- This image capturing device which can be in any orientation, captures images and uses a combination of statistics and image processing algorithms to estimate its 3D trajectory.
- a method of image-tracking comprises: (A) performing an image-capture of a scene by using an image capturing device; and
- FIG. 1 depicts an apparatus for image-tracking in accordance with an embodiment of the present technology.
- FIG. 2 is a flow chart of a method of image-tracking in accordance with an embodiment of the present technology, wherein the depth data of the scene is obtained by pre-surveying the scene.
- FIG. 3 illustrates a flow chart of a method of image-tracking in accordance with an embodiment of the present technology, wherein the depth data of the scene is obtained by using a range measurement device.
- FIG. 4 is a diagram illustrates the taking by the image capturing device, an image of a scene.
- FIG. 5 depicts a diagram illustrating the image capturing device 2D motion calculated by using the image processing algorithm in accordance with an embodiment of the present technology.
- FIG. 6 is a diagram illustrating the image capturing device height motion calculated by using the image processing algorithm in accordance with an embodiment of the present technology.
- FIG. 7 depicts a diagram illustrating the image capturing device total rotation angels (yaw, pitch and roll) calculated by using the image processing algorithm in accordance with an embodiment of the present technology.
- FIG. 1 is a block diagram 10 that illustrates an apparatus for image- tracking 22 in accordance with an embodiment of the present technology.
- the image-tracking apparatus 22 further comprises: an image capturing device 12 configured to perform an image-capture of a scene 20 in a software mode (SW) further comprising a memory 24 loaded with an image processing algorithm 25, and a general purpose processor (or a Digital Signal Processor, or a Graphic Processing Unit, etc) 26 configured to analyze the set of images by enabling the image processing algorithm 25.
- SW software mode
- a general purpose processor or a Digital Signal Processor, or a Graphic Processing Unit, etc
- the image-tracking apparatus 22 further comprises: an image capturing device 12 configured to perform an image-capture of a scene 20 in a hardware mode (HW) further comprising an ASIC chip (or FPGA chip) 27 (in analog or digital modes) configured to analyze the set of images by implementing in hardware the image processing algorithm 25.
- HW hardware mode
- ASIC chip or FPGA chip
- the image capturing device 12 is selected from the group consisting of: ⁇ a digital camera; a digital video camera; a digital camcorder; a stereo digital camera; a stereo video camera; a motion picture camera; a television camera; and a depth camera ⁇ .
- the image capturing device 12 is a light-tight box in which an image of a scene 20 is formed by a pinhole or lenses 16 at a sensor plate 32.
- Still video and digital cameras store the images in a solid-state memory 28, or on magnetic media or optical disks 28.
- Motion picture or cine cameras record movement at regular intervals in a series of frames.
- Television and video cameras record movement electronically for broadcast and storage on magnetic media or optical disks.
- Camcorders are video cameras which contain both the image sensor and recording media in a single unit.
- the focal length of lenses i.e., the distance between the rears of the lenses (when focused on infinity) the imaging device, determines the angle of view, or field of view (FOV) 18 and the size of objects as they appear on the imaging surface-sensor plate 32.
- the image is focused on that surface by adjusting the distance between the lenses and the surface.
- the lens 16 further comprises regular rectilinear lens. Rectilinear lens is a lens in which straight lines are not substantially curved or distorted. [0023] In an embodiment of the present technology, the lens 16 further comprises a fisheye lens.
- a fisheye lens is a wide-angle lens that takes in an extremely wide, hemispherical image. Fisheye lenses are often used to shoot broad landscapes. Fisheye lenses achieve extremely wide angles of view by forgoing a rectilinear image, opting instead for a special mapping (for example: equisolid angle), which gives images a characteristic convex appearance.
- the lens 16 further comprises custom-calibrated lenses.
- the image capturing device 12 further comprises a display 34 further comprising an optical display, a liquid crystal display (LCD), or a screen.
- a display 34 further comprising an optical display, a liquid crystal display (LCD), or a screen.
- LCD liquid crystal display
- the image capturing device 12 further comprises a stereo digital camera.
- a stereo camera is a type of camera with two or more lenses. This allows the camera to simulate binocular vision, and therefore gives it the ability to capture three- dimensional images, a process known as stereo photography.
- Stereo cameras may be used for making stereo views and 3D pictures for movies, or for range imaging.
- 3-D Images Ltd. located in UK, produces a 3-D Digital Stereo camera- a fully automatic, time synchronized, digital stereo camera.
- Point Grey Research Inc. located in Canada produces binoculars or multiple array cameras that can provide full field of view 3 D measurements ion an unstructured environment.
- the fundamental element of an image of an object is the pixel which describes a single point of color or a grayscale.
- Each pixel contains a series of numbers which describe its color or intensity.
- the precision to which a pixel can specify color is called its bit or color depth. The more pixels an image contains, the more detail it has the ability to describe.
- pixels per inch was introduced to relate this theoretical pixel unit to real-world visual resolution.
- PPI Picture per inch
- a "megapixel” is simply a unit of a million pixels.
- a digital camera may use a sensor array of megapixels (millions of tiny pixels) in order to produce an image.
- photo site which stores photons.
- the camera tries to assess how many photons fell into each.
- the relative quantity of photons in each cavity are then sorted into various intensity levels, whose precision is determined by bit depth (0 - 255 for an 8-bit image).
- Each cavity is unable to distinguish how much of each color has fallen in, so the above description would only be able to create grayscale images.
- One method used to extend digital sensors to capture color information is to filter light entering each cavity allowing the senor to distinguish between Red (R), Green (G) and Blue (B) lights.
- the distance from an object point 30 on the scene 20 depth to the image-based tracking device 22 is determined by using a range measuring device 14 selected from the group consisting of: ⁇ a point laser beam; a sonar; a radar; a laser scanner; and a depth camera ⁇ .
- a point laser beam range measuring device 14 can be implemented by using a blue solid-state lasers, red diode lasers, IR lasers which maybe continuously illuminated lasers, or pulsed lasers, or sequenced lasers.
- a laser scanner range measuring device 14 can be implemented by using positioning sensors offered by Sensor Intelligence website
- the Laser Scanner Model Name S10B-901 IDA having compact housing and robust IP 65 design may be used.
- a sonar range measuring device 14 can be implemented by using active sonar including sound transmitter and a receiver.
- Active sonar creates a pulse of sound, often called a "ping", and then listens for reflections (echo) of the pulse.
- This pulse of sound is generally created electronically using a sonar projector consisting of a signal generator, power amplifier and electro-acoustic transducer/array, possibly with a beam former.
- a sonar projector consisting of a signal generator, power amplifier and electro-acoustic transducer/array, possibly with a beam former.
- the pulse may be at constant frequency or a chirp of changing frequency (to allow pulse compression on reception). Pulse compression can be achieved by using digital correlation techniques.
- a radar range measuring device 14 can be implemented by using a transmitter that emits either microwaves or radio waves that are reflected by the scene 20 and detected by a receiver, typically in the same location as the transmitter.
- the image capturing device 12 further comprises a depth camera that combines taking images of an object with measuring a distance to the object.
- a depth camera can be implemented by using a ZCam video camera that can capture video with depth information. This camera has sensors that are able to measure the depth for each of the captured pixels using a principle called Time-Of-Flight. It gets 3D information by emitting pulses of infra-red light to all objects in the scene and sensing the reflected light from the surface of each object. Depth is measured by computing the time-of- flight of a ray of light as it leaves the source and is reflected by the objects in the scene 20.
- the round trip time is converted to digital code independently for each pixel using a CMOS time-to-digital converter.
- CMOS time-to-digital converter According to manufacturer 3DV Systems, the depth resolution is quite good: it can detect 3D motion and volume down to 0.4 inches, capturing at the same time full color, 1.3 megapixel video at 60 frames per second.
- the image capturing device 12 further comprises a surveying instrument 36 selected from the group consisting of: ⁇ a Global Navigation Satellite System (GNSS) surveying system; a laser plane system; and a theodolite ⁇ .
- GNSS Global Navigation Satellite System
- the scene 20 is pre surveyed and the scene distance data is used by the image-based tracking device 22 in combination with the set of images to determine the position coordinates of the image-based tracking device 22.
- a Global Navigation Satellite System (GNSS) surveying system 36 can be implemented by using a TRIMBLE R8 GNSS system that supports all GPS and GLONASS L1/L2 signals, including the new L2C and coming L5 signals of GPS and has the capacity to track up to 44 satellites.
- GNSS Global Navigation Satellite System
- a Global Navigation Satellite System (GNSS) surveying system 36 can be also implemented by using The Trimble® R7 GNSS System including a high-accuracy GPS receiver and UHF radio combined in one unit.
- Trimble R7 GNSS can be used for RTK or static surveying.
- the modular Trimble R7 GNSS System employs a separate antenna: the Trimble ZephyrTM 2 when used as a rover and the Zephyr GeodeticTM 2 when used as a base station.
- the Trimble GeoExplorer software can be used for different pathfinder scenarios.
- the Trimble GeoExplorer has the following data sheet: 1 to 3 meter GPS with integrated SBAS; a High-resolution VGA display for crisp and clear map viewing; a Bluetooth and wireless LAN connectivity options; a 1 GB onboard storage plus SD slot for removable cards. It includes Windows Mobile version 6 operating system. It is also
- a laser plane surveying system 36 can be also implemented by using a Trimble product-Spectra Precision laser GL 412 and GL 422.
- the Spectra Precision® Laser GL 412 and GL 422 Grade Lasers are cost-effective, automatic self-leveling lasers that do three jobs— level, grade and vertical alignment with plumb. Both lasers feature a 2-way, full-function remote control so one can make grade changes from anywhere on the jobsite for reduced setup time and faster operation.
- the GL 412 (single grade) and GL 422 (dual grade) lasers send a continuous, self-leveled 360-degree laser reference over entire work area, and have a wide grade range so they can be used in a variety of slope applications.
- a laser plane surveying system 36 can be also implemented by using Apache Horizon laser that emits a continuous self-leveled laser beam that is rotated to create a plane of laser light. This plane extends over a work area up to 1600 foot (500 meter) diameter.
- the reference plane is sensed by one or more laser detectors that indicate the direction to on-grade.
- a theodolite surveying system 36 can be also implemented by using Trimble ⁇ S6 DR (direct reflex) Total Station that is cable-free robotic total station and rover.
- Trimble ⁇ S6 DR direct reflex
- the method of image- tracking is implemented by using the image-based tracking device 22 of FIG. 1. More specifically, the step (A) is performed by using the image capturing device 12 to perform image-capture of a scene 20, whereas the step (B) of tracking movement of the image capturing device 12 is performed by analyzing a set of images using an image processing algorithm 25. [0048] In an embodiment of the present technology, the step (A) of performing image-capture of the scene 20 is performed in real time by using the image capturing device 12.
- the step (A) of performing image-capture of the scene 20 is pre-recorded by using the image capturing device 12.
- the step (A) of performing image-capture of the scene 20 further comprises the step (A3) of obtaining a set of depth data of the scene 20 by pre-surveying the scene 20 using the surveying instrument 36 as was fully disclosed above.
- the step (B) of tracking movement of the image capturing device 12 is performed by using the image processing algorithm 25.
- the image processing algorithm 25 allows implementation of video tracking of the image capturing device 12 by analyzing the set of images it captures.
- the image processing algorithm 25 assumes global rigid motion. By parameterizing the global optical flow with the image capturing device's 12 six degrees of freedom, an optimal global transformation between two consecutive frames can be found by solving a non-linear Least-Squares problem.
- the image processing algorithm 25 matches the optical properties of the pixels by using a frame function.
- the image processing algorithm 25 matches the depth of the two frames (instead of optical properties of the pixels) by redefinition of frame function.
- the image processing algorithm 25 can be improved by matching a combination of pixel optical properties and depth information. This can be done by either using a combined cost function, or aiding one process with the other, as fully disclosed below.
- the image processing algorithm 25 utilizes several coordinate systems: a stationary reference system; a reference system attached to the image capturing device 12; and a 2D reference system on image capturing device's sensor plane 32.
- the relation between the image capturing device-attached 3D coordinates and the 2D pixel coordinates depends on the mapping function m of the image capturing device 12.
- the mapping function takes 3D coordinates x ; in the image capturing device-attached system of the i tb frame and maps into a 2D pixel coordinates in the i & frame:
- mapping function depends on the type of the lenses.
- the mapping function m can be derived from the following equations: I
- S 11 , S v are the pixel width and height.
- ⁇ 0 , v 0 are the offsets between the optical center and sensor center.
- mapping function m can be derived from the following equations:
- mapping function m can be calibrated and stored in a numeric form.
- the range measuring device 14 is implemented by using a number of point lasers.
- the number of point lasers are usually far less than the number of pixels, the density of depth measurements for each i-th frame is likely to be much less than the pixels density. The depth for each pixel can be obtained by interpolation among these
- the range measuring device 14 is implemented by using a depth camera such as the Zcam from 3DVsystems.
- a grid of depth measurements is available with comparable resolution to that of the video frame, so that this grid of depth measurements can be used directly without further treatment.
- the range measuring device 14 is implemented by using a stereo camera. A stereo camera allows the extraction of depth info from a number of identified feature points and the rest of the pixels can be done by interpolation.
- ⁇ x ⁇ _ >Jf and ⁇ i ⁇ _ >jr which is the relative shift and rotation between frames, or, ⁇ j.
- ⁇ ( ⁇ d _, tf , ⁇ _ Jtf , ⁇ z ( ,_ y , ⁇ 1 _ !tf , ⁇ j _ )tf ,6 ⁇ s _ )tf ), which is a 6-vector having the six degrees of freedom. If the image capturing device position and attitude at frame /. is known, then solving this relative motion from f i to f j gives us the position and attitude at frame f .. In the following we will drop the subscript i - >j whenever possible.
- the image processing algorithm 25 is implemented by using Gauss-Newton formulation. To get Gauss-Newton formulation, one may expand
- V/ j ,- is the gradient image of frame f p — ⁇ - ⁇ - is the Jacobian of the geometrical transformation.
- ⁇ P (and hence the Jacobian) depends on the depth measurements ⁇ j , or, in the case of pre-surveying of depth in the stationary reference system, depends on the depth measurements z and the total image capturing device movement that leads to the frame S/ ' X Q > RJ- [0081]
- the depth measurements are obtained in the image capturing device-attached reference system (such as laser points, depth camera, stereo rig, etc)
- more depth measurements are available for frame /. because all previous frames measurements can be transformed to frame /.
- ⁇ ⁇ , Ri is known.
- the gradient image of f i t and the Jacobian at frame / are calculated while transforming j ⁇ in the iterations. Therefore 1) dt, is calculated using / j
- the convergence of the iterations depends on how "smooth" the gradient image is. If the gradient image varies on a much smaller scale than the image displacement resulted from image capturing device movement between two frames, the loop may not converge. Therefore the two frames are smoothed first before being fed into the above loop. After an approximate ⁇ is found from smoothed frames, the smoothing can be removed or reduced and a more accurate ⁇ can be obtained with the previous ⁇ as a starting point.
- R ⁇ is the transpose of the third row of the total rotation matrix R 1 at frame /.. It is the unit vector in the z direction, expressed in frame/, 's image capturing device-attached reference system.
- D (p r , D S , D* ⁇ is a 6x3 matrix with D r , D S , D h each as a 6x1 column vector.
- the image processing algorithm 25 can be implemented by using the following loop routine:
- the relation between the delta motion and the total motion of /. and/ j +1 is as follows:
- the three combination coefficients need to be adjusted when switching between the 3D reference system and projected 2D reference system, according to the depths of three vertices.
- a few (much less than the pixel numbers in the frame) depth points are obtained along with each frame in image capturing device-attached system, such as from point lasers attached to the image capturing device, or matching feature points from a stereo camera rig.
- laser point depth measurements come with each frame. They and points from previous frames are put into three categories: 1) Settling points: laser point depth measurements come with frame/. +1 . They are used only if depth matching is employed.
- Laser point depth measurements come with frame/., and laser point depth measurements come with earlier frames which have not moved out of either frames and have been transformed into frame /. 's reference system. These points are put in Delaunay Triangulation. The Delaunay vertex points are used as reference points to calculate pixel depth of by triangular interpolation.
- a grid of depth points is available with each frame, in image capturing device-attached reference system, with the same resolution as or comparable resolution to the video frame.
- depth measurements obtained with frame /. can be used directly if the resolution is the same, or can be interpolated if the resolution is lower.
- Depth measurements obtained with/ ; and /. +1 can be used in depth matching directly or after interpolation.
- FIG. 2 is a flow chart 50 of a method of image-tracking by using the device 22 of FIG. 1, wherein the depth data of the scene 20 is obtained by pre-surveying the scene.
- the method of image- tracking comprises two steps: (step 54) performing an image-capture of the scene 20 (of FIG. 1) by using an image capturing device; and (step 62) tracking movement of the image capturing device by analyzing a set of images obtained in the step 54.
- step 54 of performing an image-capture of the scene 20 is performed in real time by using the image capturing device 22 (of FIG. l)-step 56.
- step 54 is performed by pre-recording the scene 20 by using the image capturing device 22 - step 58.
- step 54 further comprises obtaining a set of depth data of the scene 20 by pre-surveying the scene- step 60.
- the image capturing device is selected from the group consisting of: ⁇ a digital camera; a digital video camera; a digital camcorder; a stereo digital camera; a stereo video camera; a motion picture camera; and a television camera ⁇ .
- step 62 of tracking movement of the image capturing device by analyzing the set of images obtained in the step 54 further comprises the step 64 of performing a rigid global transformation of the set of captured image data and the set of scene depth data into a set of 6-coordinate data; wherein the set of 6-coordinate data represents movement of the image capturing device 22 (of FIG. 1).
- FIG. 3 illustrates a flow chart 100 of a method of image-tracking, wherein the depth data of the scene is obtained by using a range measurement device 14.
- the flow chart 100 of a method of image-tracking further comprises step 104 of performing an image-capture of a scene by using an image capturing device.
- step 104 can be implemented by performing the image-capture of the scene in real time by using the image capturing device - step 106.
- step 104 can be implemented by performing the step 108 of performing an image-recording of the scene by using the image capturing device.
- the flow chart 100 of a method of image-tracking further comprises the step 110 of obtaining a set of scene depth data by using a range measurement device selected from the group consisting of: ⁇ a point laser beam; a sonar; a radar; a laser scanner; and a depth camera ⁇ .
- the step 110 is implemented by determining the set of scene depth data in an image capturing device - attached 3D- reference system by using a K-point range measurement system attached to the image capturing device- step 112.
- the step 110 is implemented by determining the depth of the object point directly for at least one image point of the object point by using an M-point range measurement system attached to the image capturing device, wherein the integer number M of depth measurements of the scene is substantially equal to the number of pixels in the frame -step 114.
- the step 110 is implemented by determining the set of scene depth data in a image capturing device -attached 3D reference system by using a feature-point range measurement system attached to the image capturing device -step 116.
- the flow chart 100 of a method of image-tracking further comprises the step 118 of tracking movement of the image capturing device by analyzing the set of images.
- the step 118 is performed by performing a rigid global transformation of the set of captured images data and the set of scene depth data into a set of 6-coordinate data; wherein the set of 6-coordinate data represents movement of the image capturing device - step 120.
- FIGs 4, 5, 6, and 7 illustrate the sample results of the image-based tracking using the apparatus 22 of FIG. 1. More specifically, FIG. 2 depicts diagram 140 illustrating the image capturing device image of the scene 20 in the sensor plane 16.
- FIG. 5 shows a diagram 150 illustrating the image capturing device 2D motion calculated by using the algorithm 25 of FIG. 1 as was fully disclosed above.
- FIG. 6 depicts a diagram 160 illustrating the image capturing device height motion calculated by using the algorithm 25 of FIG. 1 as was fully disclosed above.
- FIG. 7 shows a diagram 170 illustrating the image capturing device total rotation angels (yaw 172, pitch 174 and roll 176) calculated by using the algorithm 25 of FIG. 1 as was fully disclosed above.
- features are defined as not simply points, but also representation of regions and/or contours.
- broadly defined features can be used to substantially broaden the surface-tracking
- broadly defmed- features can be used to use scene understanding techniques to discard problematic objects (i.e. cars).
- the scene understanding techniques are methods that infer higher levels of reasoning from an image. For example, it can involve detecting the boundaries of cars, pedestrians in a scene and discarding matched features lying in those regions. Once such unwanted objects are identified; a usable region of the image is extracted. Feature matching is subsequently constrained to this region.
- the detection of unwanted objects involves object recognition including: (A) extraction of sparse features from an image; (B) clustering neighboring features together; (C) and inferring an object category for at least one given cluster.
- broadly defined- fearures can be used in the initial image analysis (such as contrast
- an initial image assessment analysis is conducted to inform the operator in the field if the images are usable or if the images have to be re-collected.
- an initial image assessment analysis comprises extracting of at least three attributes from the image: (A) saturation quality to check if an image consists mostly of one Red-Green-Blue value; (B) checking the texture quality of an image if the image is mostly blur and lacks sharp regions for feature extraction; (C) and checking an image contrast if the image is mostly dark or mostly bright, rendering the road surfaces substantially washed out.
- broadly defmed- features' can be used to initialize the surface tracking solution.
- the initial solution can be found by using broadly defmed-features and
- RANdom SAmple Consensus (RANSAC)
- [013O]RANSAC is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers.
- an outlier is an observation that is numerically distant from the rest of the data. More specifically, an outlier is defined as an outlying observation that appears to deviate markedly from other members of the sample in which it occurs.
- Outliers can occur by chance in any distribution, but they are often indicative either of measurement error or that the population has a heavy- tailed distribution. In the former case one wishes to discard them or use statistics that are robust to outliers, while in the latter case they indicate that the distribution has high kurtosis and that one should be very cautious in using tool or intuitions that assume a normal distribution.
- a frequent cause of outliers is a mixture of two distributions, which may be two distinct sub- populations, or may indicate 'correct trial' versus 'measurement error'; this is modeled by a mixture model.
- the initial solution based on broadly defmed-features and RANSAC is using a non- deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed.
- the algorithm was first published by Fischler and Bolles in 1981.
- the data consists of "inliers", i.e., data whose distribution can be explained by some set of model parameters, and "outliers" which are data that do not fit the model.
- the data can be subject to noise.
- the outliers can come, e.g., from extreme values of the noise or from erroneous measurements or incorrect hypotheses about the interpretation of data.
- RANSAC also assumes that, given a (usually small) set of inliers, there exists a procedure which can estimate the parameters of a model that optimally explains or fits this data.
- the method of using a set of broadly-defined-features to find an initial solution of the camera position as an input to surface tracking comprises the following steps: detecting a set of broadly defined features; establishing correspondences between set of broadly defined features and at least two selected frames; estimating homography between at least selected two frames using parameters of RANSAC mathematical model; deriving an initial pose of the image capturing device from the estimated homography between at least selected two frames; wherein the pose of the image-capturing device comprises position coordinates of the image-capturing device and a set of angular coordinates of the image-capturing device; and using the derived initial pose of the image capturing device as an initial solution to the surface tracking solution.
- the method of using broadly-defmed-features for finding a strict two-dimensional (strict_2D) surface tracking solution comprises: detecting a set of broadly defined features on a single tracking surface; selecting a set of coplanar broadly defined features by using parameters of RANSAC mathematical model; establishing correspondences between the set of selected coplanar broadly defined features and at least two selected frames; deriving an initial pose of the image capturing device from the homography between at least selected two frames; using the derived initial pose of the image capturing device as an initial solution to the strict two-dimensional (strict_2D) surface tracking solution; and grouping the set of coplanar features and using an area around the group of coplanar features as an input to the strict_2D surface tracking solution.
- the method of using a set of coplanar broadly defmed-features on a plurality of two-dimensional (2D) tracking surfaces for finding a substantially two-dimensional (sub_2D) surface tracking solution further comprises: detecting a set of broadly defined features on a plurality of tracking surfaces; selecting a set of coplanar broadly defined features by using parameters of the RANSAC mathematical model; establishing correspondences between the set of coplanar broadly defined features and at least two selected frames; deriving an initial pose of the image capturing device from the homography between at least two selected frames; using the derived initial pose of the image capturing device as an initial solution to a substantially two-dimensional (sub_2D) surface tracking solution; and selecting a local area around each selected coplanar broadly defined feature, grouping a plurality of the selected local areas into a two-dimensional (2D) global area and using the 2D global area as an input to the sub_2D surface tracking solution.
- the method of using a set of broadly defined-features extracted from a three dimensional (3D) for finding a solution for a substantially three-dimensional (sub_3D) surface tracking further comprises: detecting a set of broadly defined features on the 3D surface; establishing correspondences between the set of broadly defined features and at least two selected frames; estimating homography between at least two selected frames; deriving an initial pose of the image capturing device from the homography between at least selected two frames; using the derived initial pose of the image capturing device as an initial solution to the substantially three-dimensional (sub_3D) surface tracking solution; and selecting a local area around each selected broadly defined feature, grouping a plurality of the selected local areas into a three-dimensional (3D) global area and using the 3D global area as an input to the sub_3D surface tracking solution.
- exemplary systems and devices as well as various embodiments pertaining to exemplary methods of operating such systems and devices.
- one or more steps of a method of implementation are carried out by a processor under the control of computer-readable and computer- executable instructions.
- these methods are implemented via a computer.
- the computer-readable and computer-executable instructions may reside on computer useable/readable media.
- one or more operations of various embodiments may be controlled or implemented using computer-executable instructions, such as program modules, being executed by a computer.
- program modules include routines, programs, objects, components, data structures, ,, etc., that perform particular tasks or implement particular abstract data types.
- the present technology may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
- program modules may be located in both local and remote computer-storage media including memory-storage devices.
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CN102577349A (en) | 2012-07-11 |
WO2011005783A3 (en) | 2011-02-10 |
DE112010002843T5 (en) | 2012-11-29 |
US8229166B2 (en) | 2012-07-24 |
US20120195466A1 (en) | 2012-08-02 |
CN102577349B (en) | 2015-09-23 |
US9710919B2 (en) | 2017-07-18 |
US20160078636A1 (en) | 2016-03-17 |
US9224208B2 (en) | 2015-12-29 |
WO2011005783A4 (en) | 2011-03-31 |
JP2012533222A (en) | 2012-12-20 |
US20110007939A1 (en) | 2011-01-13 |
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